Background Estimation with Neural AutoRegressive Flows


Data driven background estimation is crucial for many scientific searches, including searches for new phenomena in experimental datasets. Neural autoregressive flows (NAF) is a deep generative model that can be used for general transformations, and is therefore attractive for this application.

The MLBENDER project focuses on studying how to develop such transformations that can be learned and applied to a region of interest.

Task ideas

Expected results


Python, C++, and some previous experience in Machine Learning.


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Corresponding Project

Participating Organizations